Fig 1: RPA bioinformatics in breast cancer.A Correlation matrix showing the correlation between levels of RPA1, RPA2 and RPA3 protein expressions and other DNA repair biomarkers. B Correlation matrix showing the correlation between levels of RPA1, RPA2 and RPA3 protein expressions and other endocrine-resistant biomarkers. C Comparison of RPA1 gene expression to copy number variation in TCGA-BRCA Pan cancer cohort (n = 994). GISTIC analysis is shown for changes in RPA1 mRNA levels in tumours with copy number variations for TCGA-BRCA Pan cancer cohort (n = 994). The expression data was from normalized illumina HiSeq RNA-Seq data. The copy number variations are deep deletions (>2 copies deleted), shallow deletion (few copies altered), diploid, gains (few copies gained), amplification (>2 copies gained). D DNA methylation correlations with RPA1 gene expression were performed using SMART App. The beta-values (Illumina HumanMethylation450K) and expression data were from UCSC Xena tools. The CpG correlations shown are for CpG within CpG island in promoter for RPA1 (see methods sections for more details). The percentage of RNA gene types (Ensembl MART) are shown for non-coding RNAs (lncRNA, pseudogenes, miRNAs and other RNA which include snoRNA, tRNA and MT-RNA) plus protein-coding genes are shown for (E) RNAs expressed higher in low RPA1 tumours (n = 10284 confirmed gene types) and F RNAs expressed lower in low RPA1 tumours (n = 565 confirmed gene types). G Comparison of the differential changes that showed higher expression in low RPA1, RPA2 and RPA3. The RPA components had 46% similarity of the differential changes, with the majority of RPA2 changes like RPA1.
Fig 2: Clinicopathological studies of RPA3 expression in breast cancers.A Photomicrographs showing immunohistochemical staining of RPA3 in breast cancers (scale bar “−“ = 100 µM). B Violin plot shows the mean of RPA3 expression in normal, pure DCIS and DCIS mixed tumours [The red dot represents the median, open red bar in the center represents the interquartile range, the thin red line represents the rest of the distribution, except for points that are “outliers”. On each side of the red line is a kernel density estimation to show the distribution shape of the data. Wider sections of the violin plot represent a higher probability that members of the population will take on the given value; the skinnier sections represent a lower probability.]. C Kaplan–Meier curve for RPA3 nuclear protein expression and recurrence-free interval (LRFI) in DCIS (D) a Kaplan–Meier curve for RPA3 nuclear protein expression and breast cancer-specific survival (BCSS) in the whole cohort. E Kaplan–Meier curve for RPA3 nuclear protein expression and BCSS in ER + cohort with endocrine therapy. F Kaplan–Meier curve for RPA3 nuclear protein expression and BCSS in triple negative (TN) in the whole cohort. G Kaplan–Meier curve for RPA3 mRNA expression and breast cancer-specific survival (BCSS) in the whole cohort. Survival rates were determined using Kaplan–Meier method and compared by the log-rank test. All analyses were conducted using Statistical Package for the Social Sciences (SPSS, version 22, Chicago, IL, USA) software for windows. P value of less than 0.05 was identified as statistically significant.
Fig 3: RS-35d proteomic profile in BxPC-3 cells.a Volcano plot of control vs RS-35d up- (red) and downregulated (green) proteins in BxPC-3 cells, p-value < 0.05 and log2(fold change) > 1 or < −1 were used as significance cut-off. b Venn diagram of RS-35d up- and downregulated common proteins with RAD51 interactome and ATM/ATR/DNA-PK common interactomes; Western blot analysis of FANCD2, FANCI and RPA3 expression in BxPC-3 cells treated with 40 µM RS-35d, 20 µM S-35d or 20 µM R-35d for 24 h. The images are representative Western blots. Results are normalised over β-actin expression and expressed as mean ± SD (n = 3). Statistical analysis was performed with two-way ANOVA followed by Dunnett’s multiple comparison test, with **p < 0.01 vs CTRL. c Gene Ontology (GO) cellular component, molecular functions and biological processes enriched from the analyses of the up- (red) and downregulated (green) proteins by RS-35d. d STRING functional analysis of up-regulated proteins by RS-35d. Proteins were clustered using Markov clustering (MCL), with default inflation parameter = 3; the thickness of lines representing protein-protein interactions indicates the strength of data support.
Fig 4: RPA mRNA expression and clinical ovarian cancers. A. Kaplan Meier curves of PFS and RPA1 mRNA expression. B. Kaplan Meier curves of PFS and RPA2 mRNA expression. C. Kaplan Meier curves of PFS and RPA3 mRNA expression. D. Bioinformatic analysis of genetic alterations and differential gene expression profiling for RPA complex components in TCGA-OV cohort. GISTIC analysis of RPA1 genetic alterations compared to mRNA expression levels from RNA-seq data. Pearson correlation coefficient and p-value shown. E. Venn diagram showing comparison of the differential genes identified for tumours with low versus high RPA individual components. F. Graphical representation of the gene-types assigned to the genes expressed higher in Q1 low RPA complex (n = 1787). G. genes expressed higher in Q4 high RPA complex (n = 423). Gene types were lncRNA, miRNA, pseudogenes (including transcribed and processed pseudogenes) and other RNA (tRNA, snoRNA, scaRNA and misc RNA).
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